An Adaptive Decision-making Approach for Transmission Expansion Planning Considering Risk Assessment of Renewable Energy Extreme Scenarios

Abstract

The extreme power output scenarios of renewable energy sources (RES) proposed new challenges to the safe and stable operation of the power system. Transmission expansion planning (TEP) with large-scale RES grid integration needs considering the risk of extreme scenarios. In this paper, an adaptive decision-making approach for the TEP problem based on planning-risk assessment-replanning iterative process is proposed. The method obtains massive temporal and spatial correlated wind-photovoltaic (PV) power output scenarios by generalizing the historical data to describe the uncertainties. A data-driven load loss risk assessment model (RAM) based on the power system’s actual operating state is built, referring to the degree of extreme scenario risks on the balance of supply and demand, and the probability of extreme scenario occurrence. The planning decision is progressively revised according to the risk assessment result. The Garver’s 6-bus system and the IEEE RTS 24-bus system are adopted as simulation cases. The results show that the optimal expansion plans achieve a balance between the economy and robustness, which verifies the effectiveness of the proposed method.